In this paper we study the problem of computing train trajectories in an uncertain environment in which the values of some system parameters are difficult to determine. Specifically, we consider uncertainty in traction force and train resistance, and their impact on travel time and energy consumption. Our ultimate goal is to be able to control trains such that they will arrive on-time, i.e. within the planned running time, regardless of uncertain factors affecting their dynamic or kinematic performance. We formulate the problem as a Markov decision process and solve it using a novel numerical approach which combines: (i) an off-line approximate dynamic programming (ADP) method to learn the energy and time costs over iterations, and (ii) an on-line search process to determine energy-efficient driving strategies that respect the real-time time windows, more in general expressed as train path envelope constraints. To evaluate the performance of our approach, we conducted a numerical study using real-life railway infrastructure and train data. Compared to a set of benchmark driving strategies, the trajectories from our ADP-based method reduce the probability of delayed arrival, and at the same time are able to better use the available running time for energy saving. Our results show that accounting for uncertainty is relevant when computing train trajectories and that our ADP-based method can handle this uncertainty effectively.

Train trajectory optimization for improved on-time arrival under parametric uncertainty

Railway systems must increase their performance and economic competitiveness to remain an effective and efficient transport mode. Energy efficiency goals are one of the main drivers for the future evolution of planning and operations of transport systems. An opportunity to improve energy efficiency together with reliability and feasibility of railway systems come from the huge amount of data being currently collected and available in the future. The hidden potential in large sets of data for improving energy efficiency can be fully exploited through novel, data-driven approaches. This paper discusses the relation of those future approaches with the current state of the art and challenges, highlighting natural advantages and possible weak points. We identify dimensions within the current literature describing the suitability of current approaches to embrace the data revolution, and the possible enhancements resulting from that. We refer to practical test cases based on real on-board monitoring of electric trains in Switzerland to identify current and future challenges in improving energy efficiency of train operations. We conclude with a discussion and a roadmap on the introduction of data-driven approaches for improving energy efficiency of railway systems.

Data-driven perspectives for energy efficient operations in railway systems: Current practices and future opportunities

This article presents a microscopic simulation model and corresponding energy-optimal control method for high-speed trains. The vehicle-line-coupled longitudinal dynamics model and nonlinear power loss model of the traction transmission system (TTS) for different traction force and speed pairs are discussed. Based on these detailed models, an energy-optimal control problem is formulated by systematically considering speed limitations, punctuality, and control force constraints in neutral sections of contact wire. Then, the formulation is transformed into a multiple phases’ optimal control model based on some key positions, and the hp-adaptive pseudospectral method is applied to obtain the optimal results. The proposed dynamics model and the energy consumption model are validated by the comparison of model predictions and measured data. The simulation results show that the energy efficiency of the optimal results with a nonlinear power loss model is improved compared to the traditional method with constant efficiency of the TTS. The energy consumption can be reduced by 12.67%, comparing optimal speed profiles against measured results on commercial lines of China. Finally, the influences of gradients are analyzed, and the coast-traction pairs can replace cruising strategy on downhill sections.

Modeling and Energy-Optimal Control for High-Speed Trains

Energy-efficient train control (EETC) has been studied a lot over the last decades, because it contributes to cost savings and reduction of CO2 emissions. The aim of EETC is to minimize total traction energy consumption of a train run given the running time in the timetable. Most research is focused to apply mechanical braking on this problem. However, current trains are able to use regenerative braking, which leads to another optimal driving strategy compared to mechanical braking. Research on EETC with a realistic nonlinear bounded model for regenerative braking or a combination between regenerative and mechanical braking is limited. The aim of this paper is to compare the difference between the EETC with regenerative and/or mechanical braking. First, we derive the optimal control structure for the problems with different braking combinations. Second, we apply the pseudospectral method on different scenarios where we investigate the effect of varying speed limits and gradients on the different driving strategies. Results indicate that compared to pure mechanical braking, combined regenerative and mechanical braking leads to a driving strategy with higher energy savings, a lower optimal cruising speed, a shorter coasting phase and a higher speed at the beginning of the braking phase. In addition, a nonlinear bounded regenerative braking curve leads to a different driving strategy compared to a constant braking rate that is commonly used in literature. We show that regenerative braking at a constant braking rate overestimates the total energy savings.

Energy-efficient train control using nonlinear bounded regenerative braking

Reducing fuel consumption and related emissions through optimal sizing of energy storage systems for diesel-electric trains

Driver advisory systems, instructing the driver how to control the train in an energy efficient manner, is one the main tools for minimizing energy consumption in the railway sector. There are many ...

A driver advisory system with dynamic losses for passenger electric multiple units

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Robert Skoglund

Author Tools

Chat about Author

Papers

A driver advisory system with dynamic losses for passenger electric multiple units